| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| import utils3d | |
| from PIL import Image | |
| from ..renderers import OctreeRenderer, GaussianRenderer, MeshRenderer | |
| from ..representations import Octree, Gaussian, MeshExtractResult | |
| from ..modules import sparse as sp | |
| from .random_utils import sphere_hammersley_sequence | |
| def yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitchs, rs, fovs): | |
| is_list = isinstance(yaws, list) | |
| if not is_list: | |
| yaws = [yaws] | |
| pitchs = [pitchs] | |
| if not isinstance(rs, list): | |
| rs = [rs] * len(yaws) | |
| if not isinstance(fovs, list): | |
| fovs = [fovs] * len(yaws) | |
| extrinsics = [] | |
| intrinsics = [] | |
| for yaw, pitch, r, fov in zip(yaws, pitchs, rs, fovs): | |
| fov = torch.deg2rad(torch.tensor(float(fov))).cuda() | |
| yaw = torch.tensor(float(yaw)).cuda() | |
| pitch = torch.tensor(float(pitch)).cuda() | |
| orig = ( | |
| torch.tensor( | |
| [ | |
| torch.sin(yaw) * torch.cos(pitch), | |
| torch.cos(yaw) * torch.cos(pitch), | |
| torch.sin(pitch), | |
| ] | |
| ).cuda() | |
| * r | |
| ) | |
| extr = utils3d.torch.extrinsics_look_at( | |
| orig, | |
| torch.tensor([0, 0, 0]).float().cuda(), | |
| torch.tensor([0, 0, 1]).float().cuda(), | |
| ) | |
| intr = utils3d.torch.intrinsics_from_fov_xy(fov, fov) | |
| extrinsics.append(extr) | |
| intrinsics.append(intr) | |
| if not is_list: | |
| extrinsics = extrinsics[0] | |
| intrinsics = intrinsics[0] | |
| return extrinsics, intrinsics | |
| def render_frames( | |
| sample, | |
| extrinsics, | |
| intrinsics, | |
| options={}, | |
| colors_overwrite=None, | |
| verbose=True, | |
| **kwargs, | |
| ): | |
| if isinstance(sample, Octree): | |
| renderer = OctreeRenderer() | |
| renderer.rendering_options.resolution = options.get("resolution", 512) | |
| renderer.rendering_options.near = options.get("near", 0.8) | |
| renderer.rendering_options.far = options.get("far", 1.6) | |
| renderer.rendering_options.bg_color = options.get("bg_color", (0, 0, 0)) | |
| renderer.rendering_options.ssaa = options.get("ssaa", 4) | |
| renderer.pipe.primitive = sample.primitive | |
| elif isinstance(sample, Gaussian): | |
| renderer = GaussianRenderer() | |
| renderer.rendering_options.resolution = options.get("resolution", 512) | |
| renderer.rendering_options.near = options.get("near", 0.8) | |
| renderer.rendering_options.far = options.get("far", 1.6) | |
| renderer.rendering_options.bg_color = options.get("bg_color", (0, 0, 0)) | |
| renderer.rendering_options.ssaa = options.get("ssaa", 1) | |
| renderer.pipe.kernel_size = kwargs.get("kernel_size", 0.1) | |
| renderer.pipe.use_mip_gaussian = True | |
| elif isinstance(sample, MeshExtractResult): | |
| renderer = MeshRenderer() | |
| renderer.rendering_options.resolution = options.get("resolution", 512) | |
| renderer.rendering_options.near = options.get("near", 1) | |
| renderer.rendering_options.far = options.get("far", 100) | |
| renderer.rendering_options.ssaa = options.get("ssaa", 4) | |
| else: | |
| raise ValueError(f"Unsupported sample type: {type(sample)}") | |
| rets = {} | |
| for j, (extr, intr) in tqdm( | |
| enumerate(zip(extrinsics, intrinsics)), desc="Rendering", disable=not verbose | |
| ): | |
| if not isinstance(sample, MeshExtractResult): | |
| res = renderer.render(sample, extr, intr, colors_overwrite=colors_overwrite) | |
| if "color" not in rets: | |
| rets["color"] = [] | |
| if "depth" not in rets: | |
| rets["depth"] = [] | |
| rets["color"].append( | |
| np.clip( | |
| res["color"].detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255 | |
| ).astype(np.uint8) | |
| ) | |
| if "percent_depth" in res: | |
| rets["depth"].append(res["percent_depth"].detach().cpu().numpy()) | |
| elif "depth" in res: | |
| rets["depth"].append(res["depth"].detach().cpu().numpy()) | |
| else: | |
| rets["depth"].append(None) | |
| else: | |
| res = renderer.render(sample, extr, intr) | |
| if "normal" not in rets: | |
| rets["normal"] = [] | |
| rets["normal"].append( | |
| np.clip( | |
| res["normal"].detach().cpu().numpy().transpose(1, 2, 0) * 255, | |
| 0, | |
| 255, | |
| ).astype(np.uint8) | |
| ) | |
| return rets | |
| def render_video( | |
| sample, resolution=512, bg_color=(0, 0, 0), num_frames=300, r=2, fov=40, **kwargs | |
| ): | |
| yaws = torch.linspace(0, 2 * 3.1415, num_frames) | |
| pitch = 0.25 + 0.5 * torch.sin(torch.linspace(0, 2 * 3.1415, num_frames)) | |
| yaws = yaws.tolist() | |
| pitch = pitch.tolist() | |
| extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics( | |
| yaws, pitch, r, fov | |
| ) | |
| return render_frames( | |
| sample, | |
| extrinsics, | |
| intrinsics, | |
| {"resolution": resolution, "bg_color": bg_color}, | |
| **kwargs, | |
| ) | |
| def render_multiview(sample, resolution=512, nviews=30): | |
| r = 2 | |
| fov = 40 | |
| cams = [sphere_hammersley_sequence(i, nviews) for i in range(nviews)] | |
| yaws = [cam[0] for cam in cams] | |
| pitchs = [cam[1] for cam in cams] | |
| extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics( | |
| yaws, pitchs, r, fov | |
| ) | |
| res = render_frames( | |
| sample, | |
| extrinsics, | |
| intrinsics, | |
| {"resolution": resolution, "bg_color": (0, 0, 0)}, | |
| ) | |
| return res["color"], extrinsics, intrinsics | |
| def render_snapshot( | |
| samples, | |
| resolution=512, | |
| bg_color=(0, 0, 0), | |
| offset=(-16 / 180 * np.pi, 20 / 180 * np.pi), | |
| r=10, | |
| fov=8, | |
| **kwargs, | |
| ): | |
| yaw = [0, np.pi / 2, np.pi, 3 * np.pi / 2] | |
| yaw_offset = offset[0] | |
| yaw = [y + yaw_offset for y in yaw] | |
| pitch = [offset[1] for _ in range(4)] | |
| extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics( | |
| yaw, pitch, r, fov | |
| ) | |
| return render_frames( | |
| samples, | |
| extrinsics, | |
| intrinsics, | |
| {"resolution": resolution, "bg_color": bg_color}, | |
| **kwargs, | |
| ) | |